This invention relates to communications systems, data transmission, modulation, coding, a bandwidth efficient advanced modulation waveform, and more specifically a channel equalization algorithm for use in these systems.
In digital communications systems such as cellular and PCS (personal communications systems), computer communications systems, and SATCOM (satellite communications) systems digital data is modulated by a modem onto a signal to be transmitted over a communications channel. The communications channel may add noise, interference, multipath, fading, and other corrupting influences that may result in loss of the data when demodulated at a receiver in the communications link. Channel coding has been used in communications systems for many years to detect and/or correct data bit errors by introducing redundant bits. The use of channel coding results in a reduction of data rate or an increase in required bandwidth due to the additional redundant bits.
Block codes and convolutional codes are two types of channel codes commonly used in the art of channel coding. A block code is an error detection and/or correction code in which an encoded block of data consists of n coded bits, containing k information bits (k<n) and n-k redundant check bits to detect and/or correct most errors. Types of block codes known in the art include Hamming codes, Golay code, BCH codes, and Reed Solomon codes.
Convolutional codes are widely used in the communications art to provide real time error correction. Convolutional codes continuously convert an entire data stream to encode the k information bits. The encoded bit stream depends on the current information bits and also on the previous input information bits. With a convolutional code, k information bits are coded into n coded bits in an encoder with m memory stages that store the state information of the encoder. A constraint length K of a convolutional code is defined as m+1 and a code rate r as k/n. The well-known Viterbi algorithm is commonly used in convolutional coding.
Recent advances in the art of coding to further improve error detection and correction while reducing bandwidth requirements include turbo codes and trellis coded modulation. Trellis coded modulation combines coding and modulation into one operation. By combing coding and modulation, redundancy can be obtained with no reduction in data rate or increase in bandwidth.
Continuous phase modulation (CPM) is being applied in communications due to its bandwidth efficiency and constant envelope characteristics. With CPM, the modulated signal phase transitions are smoothed. With BPSK (binary phase shift keying) a logic one is transmitted as one phase of a modulated signal and a logic zero is transmitted as a 180-degree shifted phase with a sharp transition in phase. This sharp phase transition results in broadening of the transmitted spectrum. With CPM the phase of the transmitted signal makes smooth phase changes over the bits of the modulating digital signal. An example of CPM currently in use is MSK (minimum shift keying).
Turbo codes allow reliable transmission of data across a communications channel near the theoretical limit predicted by Claude Shannon. A turbo code is generated by a parallel concatenation of two component convolutional codes separated by an interleaver. Turbo decoding uses a soft decoder at the input followed by an inverse interleaver and a second soft decoder. The output of the second soft decoder feeds back to the input of the first soft decoder through an interleaver. The data is passed through the turbo decoder in several iterations with each pass improving the quality of error correction.
A new data communications waveform has been developed by Rockwell-Collins called BEAM (bandwidth efficient advanced modulation). A goal of the BEAM waveform development is to increase the throughput of a typical 25-kHz UHF SATCOM channel by a factor of five over the MIL-STD-188-181A FSK waveform, while maintaining a reasonable Eb/No. The throughput will thus increase from the current 16 kbps to 80 kbps. The BEAM waveform must operate with current UHF satellites and have a constant envelope. The constant envelope requirement is important for this application because all current UHF satellites use saturating amplifiers. In addition, most, if not all, of the UHF user terminals utilize saturating amplifiers for power efficiency. The BEAM waveform jointly combines coded CPM modulation with turbo decoding. Although the BEAM waveform has been invented for UHF SATCOM applications, the BEAM waveform concept can be extended to any type of AWGN (additive white Gaussian noise) communications channel. Because of the underlying CPM modulation, it is particularly useful to those communications systems that rely on saturated amplifiers.
Channel equalization is widely used in communications systems to compensate for communications channel degradation of the transmitted signal in phase and amplitude. Equalizers compensate for the effects of frequency dependent phase and amplitude distortion caused by multipath and transmit and receive filters. In digital communications systems, the digital data can be subject to intersymbol interference (ISI) that may be caused by the transmitter filter, distortion, communications channel conditions, and the receiver filter. The communications channel acts like an analog low pass filter with a frequency response that smears a digital signal such that received pulses that correspond to different symbols are not separable. An equalizer can be used to compensate for the effects of the communications channel by performing as a filter with a frequency response that approximates the inverse of the communications channel frequency and filter responses.
Several methods of determining the filter characteristics of an equalizer filter are known in the art. One method is automatic synthesis where the equalizer compares a received signal to a stored copy of an undistorted training signal. Comparing the two signals results in an error signal that can then be used to determine coefficients of the inverse filter. The automatic synthesis method is used in zero-force equalization (ZFE) and minimum mean-square error (MMSE) equalization. A shortcoming of the automatic synthesis method is the need to transmit the training signal.
Another method of calculating the filter coefficients for an equalizer filter is known as adaptive equalization. With the adaptive method, the equalizer attempts to minimize an error signal. In the case of decision-directed adaptive equalization, the error signal is based on the comparison of the equalizer output and an estimate of the transmitted signal generated in a decision device. Examples of decision-directed adaptive equalizers include the least mean square (LMS) algorithm, also called the stochastic gradient algorithm, and the recursive least squares (RLS) algorithm. The decision device makes an estimate of the transmitted signal from the output of the equalizer, and determines which, of a constellation of possible signals, was most likely to have been transmitted.
Many communication systems employ memory either implicitly in the modulation, such as continuous-phase modulation (CPM), or explicitly in a code, such as a convolutional code. In these systems, the receiver typically detects the transmitted sequence by a Viterbi or APP (a posterior probability) decoder that, in turn, operate on a trellis description of the memory in the system. These trellis-based algorithms typically require considerable time to make reliable decisions. Many subsystems within the demodulator require decisions from the demodulator and/or decoder in a timely manner. Decision-directed adaptive equalizers and decision-feedback equalizers represent two such classes of subsystems. In addition, recent coding advances, in particular iterative decoding of concatenated systems like turbo codes, allow the receiver to operate at unprecedentedly low signal-to-noise ratios (SNRs). At very low SNRs, the decoded decisions are initially very unreliable, which adversely affects the operation of the subsystems requiring these decisions. Typically, the subsystem, e.g., equalizer, has no indication of the confidence of the decisions.
What is needed is an algorithm for supplying timely decisions with confidence values (“soft decisions”) to an adaptive decision-directed equalizer in a trellis-based communication system. The channel equalizer may use a simplified version of a demodulator that supplies soft decisions in parallel with the complete demodulator. A method for using the soft decisions from the decoder within the equalizer is required.